Table 2.—

Candidate Model Specifications

ID	Description of Predictors	Rationale	Functional Form	Category-Specific Degrees of Freedom
				Sleep	Stress	Caffeine/Alcohol	Weather
1L	Raw levels: Lag-1	Used raw exposure values from previous day. Simplest; commonly found in the literature.11,50,51	Linear	6	2	4	8
1S			Spline	9	3	5	12
2L	Change in level: Lag-2 to Lag-1	Used change in exposure level from one day to the next, to predict migraine on the third day. Changes in some factors may predict migraine as well or better than the raw values.10,45,52,53	Linear	6	2	4	8
			Spline	9	3	5	12
2S
3	Raw levels: Lag-1 and Lag-2	Extension of model 1L; allowed various timing of maximal correlation between exposure and migraine risk.	Linear	9	3	6	8
4	Change in level: Lag-2 to Lag-1, and Lag-3 to Lag-2	Extension of model 2L; allowed various timing of maximal correlation between change in exposure and migraine risk.	Linear	9	3	6	12
5L	Cumulative: Mean of Lag 1, 2, and 3	Used mean exposure value over lags 1–3, as a low-dimensional summary of the cumulative exposure. Cumulative levels of stress and sleep deprivation have been correlated with other transient health outcomes,54–56 but have not been well studied with respect to migraine risk.	Linear	6	2	4	8
5S
			Spline	9	3	5	12
6	Change: Max absolute 1-day change over Lags 1–3	Summarized change in exposure over the past 3 days with the maximum absolute 1-day change between either lag 4 to lag 3, lag 3 to lag 2, or lag 2 to lag 1.52	Linear	6	2	4	8

The candidate model specifications shown above were based on the day-level trigger exposures defined in Table 1. These models were estimated with multilevel logistic regression and their AICs compared for each of the 4 categories. From each category, the predictors from the best-fitting model specification were included in the primary prediction model combining all categories.